Artificial neuronal networks are essentially composed of neurons interconnected to one another by synapses. The synapses are conventionally formed from digital memories or resistive components the conductance of which varies depending on the voltage or current applied to their terminals.
A learning rule conventionally used in pulsed neural networks is the spike-timing dependent plasticity (STDP) rule. This is a biologically inspired rule the objective of which is to reproduce the learning function performed by biological neurons and synapses. For such a rule to be implemented, the conductance of the synapses must vary depending on the relative arrival times of “pre-” and “post-” synaptic pulses transmitted by the neurons connected to the input and output of the synapse, respectively. With the STDP rule, the conductance of a synapse is increased if its post-synaptic neuron is activated after its pre-synaptic neuron, and decreased in the opposite case. Furthermore, the conductance variation also depends on the exact delay time between pulses generated following activation of the neurons. Typically, the larger the delay time, the smaller the variation in conductance will be.
To implement an STDP rule or any other unsupervised learning method, it is necessary for the conductance of the memristive components that form the artificial synapses of the network to be able to vary gradually, both upwards and downwards, depending on the voltage or the current applied to their terminals. Furthermore, it is desirable if this can occur without prior knowledge of the conductance state of the synapse.
Cells using conductive bridging random access memory (CBRAM) technology are for example employed to form the synapses of the neuronal network. These cells are conventionally grouped in a memory matrix.
A CBRAM cell possesses an electrode made of an electrochemically active metal such as silver (Ag) or copper (Cu). High-mobility Ag+ cations drift in a conductive layer, for example made of germanium sulphide (GeS2), and are rejected at an anode, for example one made of tungsten (W). This leads to the growth of Ag dendrites, i.e. to the formation of a high-conductivity filament. Once this filament has formed, the CBRAM circuit is in what is called the ON state. The resistance of the circuit is then very low. Therefore, its conductance G=1/R is very high.
When a voltage of inverse polarity is applied to the terminals of the CBRAM cell, the conductive bridge is dissolved electrochemically and the circuit is then in the OFF position. System reset is then spoken of. In the OFF position, the resistance of the circuit is then high. Therefore, its conductance G=1/R is low.
Using CBRAM cells in neuromorphic systems has a number of advantages. Specifically, the manufacture of the system is made easier, and the system is CMOS compatible. The choice of this type of cells is very advantageous for the design of biologically inspired low-power systems.
In the prior art, multi-level programming of CBRAM cells has been proposed in order to imitate the plasticity of biological synapses. However, this approach implies that each neuron must generate pulses the amplitude of which increases while still preserving a history of the prior state of the synaptic cells, thereby leading to additional complexity in the neuromorphic system.